Your eval set is probably in your training set — here's how to check in ten minutes

You fine-tune a model, run your benchmark, and the score jumps six points. Before you write that up, there's one question worth ten minutes: how many of those benchmark examples were in the training data? If the answer is "some", part of that six points is a measurement of memory rather than capability — and there is no way to separate the two after the fact. This is train/test contamination. It's one of the most common and least discussed reasons an offline number fails to reproduce in production, and it is almost never introduced deliberately. How it gets in Nobody copies their test set into training on purpose. It happens through ordinary steps: Merging public datasets. Two datasets that look unrelated often share a source. Instruction-tuning collections are especially prone to this — many are recombinations of the same handful of seed sets. Splitting after augmentation. Paraphrase or template-expand first, split second, and variants of one item land on both sides. The split looks random. It isn't. Re-scraping. Your eval set came from a site in March. Your training crawl hit the same site in June. Synthetic data from a model that saw the benchmark. You may be distilling memorised answers straight into your training file. Datasets that grow. Eval was frozen a year ago; train has been appended to weekly by three people since, and nobody re-checked. The pattern: contamination arrives with pipeline changes . That's why a one-off audit doesn't stay true, and why this belongs in CI rather than in a notebook you ran once. Why it's worse than the percentage suggests If 5% of your eval set is contaminated and the model scores near-perfectly on that slice, it can move the headline number by several points — often the same magnitude as the improvement you're trying to demonstrate. Worse, it biases decisions , not just reporting. You pick checkpoints, hyperparameters and data mixes by comparing eval scores. Contamination rewards whichever run memorised more, which is usually